Security Incidents Analysis: Global Patterns and Trends
1 Introduction
This analysis explores patterns and trends in global security incidents, identifying hotspots and tracking how they’ve evolved over time. By examining data on security incidents from around the world, we can better understand which regions face the greatest challenges and how these challenges have shifted in recent years.
Security incidents can range from physical attacks to cyber threats, and their distribution isn’t uniform across the globe. Understanding these patterns can help security professionals, policymakers, and researchers allocate resources effectively and develop targeted strategies to mitigate risks.
The following analysis uses data visualization techniques to uncover insights about:
The geographic distribution of security incidents
How incident patterns have changed over time
Which countries represent security hotspots, both historically and recently
The nature and impact of different incident types
1.1 Dataset Overview
Our analysis begins with a cleaned dataset of security incidents collected from various sources. Let’s examine the scope of our data:
Code
print(f"Dataset contains {len(df)} incidents across {df['country'].nunique()} countries")print(f"Time period covered: {df['year'].min()} to {df['year'].max()}")
Dataset contains 4314 incidents across 95 countries
Time period covered: 1997 to 2025
This extensive dataset allows us to perform comprehensive analysis across both geographic and temporal dimensions. The data has been preprocessed to ensure consistency in country names, coordinate information, and incident classifications.
2 Global Distribution of Security Incidents
Security incidents aren’t distributed evenly across the world. Some regions experience higher concentrations due to various factors including geopolitical tensions, economic disparities, and historical conflicts. Visualizing this distribution helps us identify global patterns.
2.1 Interactive Global Incident Map
The map below displays incidents across the globe, with colors indicating the severity based on the number of people affected:
Blue: No reported casualties
Green: 1-5 affected individuals
Orange: 6-20 affected individuals
Red: More than 20 affected individuals
Clustering is used to manage dense areas where multiple incidents occurred in close proximity.
Code
def create_incidents_map(data):""" Create an interactive folium map with clustered markers for security incidents. Args: data: DataFrame containing incident data with latitude and longitude coordinates Returns: folium.Map: Interactive map with clustered markers """# Calculate center coordinates for the map center_lat = data['latitude'].mean() center_lon = data['longitude'].mean()# Create base map incidents_map = folium.Map(location=[center_lat, center_lon], zoom_start=2)# Add marker cluster marker_cluster = MarkerCluster().add_to(incidents_map)# Filter for valid coordinates valid_coords = data[data['latitude'].notna() & data['longitude'].notna()]# Define color function based on number of affected peopledef get_color(affected):if pd.isna(affected) or affected ==0:return palette["secondary"] # Blueelif affected <=5:return palette["primary"] # Orangeelif affected <=20:return"#FF9A3C"# Darker orangeelse:return palette["danger"] # Red# Add markers for each incidentfor _, row in valid_coords.iterrows(): popup_text =f""" <b>Country:</b> {row['country']}<br> <b>Year:</b> {row['year']}<br> <b>Total Affected:</b> {row['total_affected']}<br> <b>Attack Type:</b> {row['means_of_attack'] if'means_of_attack'in row and pd.notna(row['means_of_attack']) else'Unknown'}<br> """ folium.CircleMarker( location=[row['latitude'], row['longitude']], radius=5, popup=folium.Popup(popup_text, max_width=300), fill=True, fill_opacity=0.7, color=get_color(row['total_affected']), fill_color=get_color(row['total_affected']) ).add_to(marker_cluster)return incidents_map
Code
# Create and display the global incidents mapglobal_incidents_map = create_incidents_map(df)map_filename ="images/global_security_incidents_map.html"global_incidents_map.save(map_filename)global_incidents_map
Make this Notebook Trusted to load map: File -> Trust Notebook
The interactive map reveals several important patterns:
Incidents tend to cluster in certain regions, particularly in conflict zones and areas with political instability
Urban centers often show higher concentrations of incidents
The distribution of high-severity incidents (red markers) isn’t uniform, suggesting regional differences in the nature of security threats
You can zoom in on specific regions and click on individual markers to get more details about each incident.
3 Temporal Trends in Security Incidents
Security landscapes evolve over time. By examining how incident patterns change year by year, we can identify emerging hotspots and areas where security might be improving.
3.1 Incidents by Country Over Time
The animated choropleth map below shows how the distribution of security incidents has shifted over the years. Darker colors indicate higher numbers of incidents.
Code
incidents_by_year_country = df.groupby(['year', 'country']).size().reset_index(name='incidents')year_totals = incidents_by_year_country.groupby('year')['incidents'].sum().reset_index().sort_values('year')# Create a custom orange-blue color scale using our palette colorscustom_colorscale = [ [0, "#FFFFFF"], # White for zero [0.5, palette["primary"]], # Orange for mid values [1, "#B36D00"] # Darker orange for high values]fig = px.choropleth( incidents_by_year_country, locations='country', locationmode='country names', color='incidents', animation_frame='year', color_continuous_scale=custom_colorscale, range_color=[0, incidents_by_year_country['incidents'].max()], height=600)fig.update_layout( title={'text': 'Security Incidents by Country Over Time','y': 0.95,'x': 0.5,'xanchor': 'center','yanchor': 'top','font': {'size': 24} }, coloraxis_colorbar=dict(title='Number of Incidents'), geo=dict(showframe=False, showcoastlines=True, projection_type='natural earth'), margin=dict(t=100))fig.layout.updatemenus[0].buttons[0].args[1]['frame']['duration'] =1000fig.layout.updatemenus[0].buttons[0].args[1]['transition']['duration'] =500for i, year inenumerate(year_totals['year'].unique()): year_total = year_totals.loc[year_totals['year'] == year, 'incidents'].values[0] fig.frames[i].layout.annotations = [dict( x=0.5, y=0.87, xref='paper', yref='paper', text=f'Total Incidents in {year}: {year_total}', showarrow=False, font=dict(size=18) ) ]latest_year = year_totals['year'].max()latest_total = year_totals.loc[year_totals['year'] == latest_year, 'incidents'].values[0]fig.update_layout(annotations=[dict( x=0.5, y=0.87, xref='paper', yref='paper', text=f'Total Incidents in {latest_year}: {latest_total}', showarrow=False, font=dict(size=18) )])fig.write_html("images/interactive_incidents_over_time.html")fig.show()
This visualization reveals several key insights:
The global distribution of security incidents has shifted significantly over time
Some countries that were previously security hotspots have shown improvement
New areas of concern have emerged in recent years
The total number of recorded incidents shows notable year-to-year variation
You can use the play button to animate the map through time, or manually select specific years using the slider.
3.2 Annual Incident Trends
To better understand the overall temporal pattern of security incidents, we can examine the yearly totals across all countries.
Code
# Aggregate incidents by yearyear_incidents = df.groupby('year').size().reset_index(name='incidents')year_incidents['year'] = year_incidents['year'].astype(str)# Create interactive bar chartfig2 = px.bar( year_incidents, x='year', y='incidents', labels={'incidents': 'Number of Incidents', 'year': 'Year'}, height=400)# Update bar colors to primary orangefig2.update_traces(marker_color=palette["primary"])# Configure layoutfig2.update_layout( title={'text': 'Security Incidents by Year','y': 0.95,'x': 0.5,'xanchor': 'center','yanchor': 'top' }, xaxis=dict(rangeslider=dict(visible=True), type='category'), bargap=0.1, template='plotly_white')# Save and display figurefig2.write_html("images/interactive_yearly_incidents_barchart.html")fig2.show()
The interactive bar chart shows:
Years with notable spikes in security incidents
Overall trends in global security over time
Periods of relative stability and instability
The rangeslider at the bottom allows you to focus on specific time periods for more detailed analysis. Several factors might explain the patterns observed, including:
Major global events and conflicts
Changes in reporting practices
Implementation of new security measures
Shifts in geopolitical landscapes
4 Identifying Security Hotspots
Not all countries experience security incidents at the same rate. By identifying which nations have faced the highest numbers of incidents, we can focus attention on areas that may require additional security resources and intervention.
4.1 Countries with Most Incidents: All-Time Analysis
First, let’s look at which countries have experienced the most security incidents over the entire period covered by our dataset:
Code
# Get total incidents by country for all timetotal_by_country = df.groupby('country').size().reset_index(name='total_incidents')total_by_country = total_by_country.sort_values('total_incidents', ascending=False)top15_countries = total_by_country.head(8)# Create bar chart for all-time top countriesfig_top_all_time = px.bar( top15_countries, x='country', y='total_incidents', labels={'total_incidents': 'Number of Incidents', 'country': 'Country'}, height=450)# Update to use primary orange colorfig_top_all_time.update_traces(marker_color=palette["primary"])# Configure layoutfig_top_all_time.update_layout( title={'text': 'Top Countries by Security Incidents (All Time)','y': 0.95,'x': 0.5,'xanchor': 'center','yanchor': 'top','font': {'size': 20} }, xaxis={'categoryorder': 'total descending', 'tickangle': 45})# Display the figurefig_top_all_time.show()# Save figurefig_top_all_time.write_html("images/top_countries_all_time.html")
This visualization highlights the countries that have historically been most affected by security incidents. Several factors might contribute to a country appearing on this list:
Long-standing regional conflicts
Political instability
Higher population (which can increase the absolute number of incidents)
More comprehensive reporting of incidents
4.2 Countries with Most Incidents: Recent Trends
Historical patterns don’t always reflect current realities. To identify emerging security hotspots, we need to focus on more recent data. The following analysis examines incident patterns over the past 10 years:
Code
# Get incidents for the last 10 yearscurrent_year = df['year'].max()ten_years_ago = current_year -10recent_df = df[df['year'] >= ten_years_ago]# Calculate top countries for recent periodrecent_by_country = recent_df.groupby('country').size().reset_index(name='recent_incidents')recent_by_country = recent_by_country.sort_values('recent_incidents', ascending=False)top15_recent = recent_by_country.head(8)# Create bar chart for recent top countriesfig_top_recent = px.bar( top15_recent, x='country', y='recent_incidents', labels={'recent_incidents': 'Number of Incidents', 'country': 'Country'}, height=500)# Update to use primary orange colorfig_top_recent.update_traces(marker_color=palette["primary"])# Configure layoutfig_top_recent.update_layout( title={'text': f'Top Countries by Security Incidents (Last 10 Years: {ten_years_ago}-{current_year})','y': 0.95,'x': 0.5,'xanchor': 'center','yanchor': 'top','font': {'size': 20} }, xaxis={'categoryorder': 'total descending', 'tickangle': 45})# Display the figurefig_top_recent.show()# Save figurefig_top_recent.write_html("images/top_countries_recent.html")
This recent trends analysis shows:
Countries that have experienced deteriorating security situations in the past decade
Nations that continue to face persistent security challenges
Emerging hotspots that may not have appeared in historical data
By comparing this visualization with the all-time analysis, we can identify significant shifts in global security patterns.
5 Types of Injuries and Their Contexts
Understanding the nature of injuries in security incidents provides critical insights for medical preparedness and response planning. Different attack methods produce different injury patterns, each requiring specific medical interventions.
Code
# Create a mapping between attack types and injury categoriesif'means_of_attack'in df.columns:# Group by attack method attack_counts = df['means_of_attack'].value_counts().reset_index() attack_counts.columns = ['Attack Type', 'Count'] attack_counts = attack_counts.sort_values('Count', ascending=False).head(10)# Create horizontal bar chart fig_injuries = px.bar( attack_counts, y='Attack Type', x='Count', title='Most Common Attack Types and Associated Injuries', height=500, orientation='h' )# Set all bars to primary color fig_injuries.update_traces(marker_color=palette["primary"])# Configure layout fig_injuries.update_layout( yaxis={'categoryorder': 'total ascending'}, xaxis_title="Number of Incidents", yaxis_title=None, title={'y': 0.95,'x': 0.5,'xanchor': 'center','yanchor': 'top' } ) fig_injuries.show()
5.1 Common Injury Types and Their Characteristics
Different attack methods produce distinct injury patterns that require specific medical responses:
5.1.1 Explosive-Related Injuries
Blast Injuries: Primary blast injuries affect air-filled organs (lungs, ears, GI tract) through pressure waves
Fragmentation Wounds: Secondary blast injuries from flying debris cause penetrating trauma
Blunt Trauma: Tertiary blast injuries from being thrown by blast wind
Burns: Quaternary blast injuries from heat and flames
Common contexts: Suicide bombings, IEDs, landmines, aerial bombardment
5.1.2 Gunshot Wounds
Ballistic Trauma: High-velocity projectiles create cavitation and tissue destruction
Penetrating Wounds: Damage to vital organs, vessels, and structures
Orthopedic Injuries: Bone fractures and joint destruction
Common contexts: Armed conflict, targeted attacks, crossfire situations
5.1.3 Blunt Force Trauma
Contusions and Lacerations: Soft tissue injuries
Orthopedic Injuries: Fractures, dislocations, and crush injuries
Internal Injuries: Damage to internal organs without external wounds
Common contexts: Physical assaults, vehicle attacks, detention
5.1.4 Chemical/Biological Injuries
Respiratory Damage: Pulmonary edema, airway burns
Skin and Eye Irritation: Chemical burns, blistering
Systemic Toxicity: Organ failure, neurological symptoms
Common contexts: Chemical weapons deployment, industrial accidents
5.2 When Different Injury Types Occur
Certain injury patterns are more likely in specific contexts:
Combat/Crossfire Situations: Predominantly ballistic trauma and explosive injuries
Targeted Attacks: Often involve firearms or explosive devices
Civil Unrest: Combination of blunt trauma, less-lethal weapons effects
Terrorist Incidents: Complex injury patterns from explosives and multiple attack methods
Detention: Predominantly blunt trauma and torture-related injuries
Understanding these patterns allows for appropriate medical preparedness, including: - Proper training of medical personnel - Stockpiling appropriate medical supplies - Developing contextually appropriate evacuation protocols - Creating specialized treatment facilities in high-risk areas
6 Attack Contexts
Examining the contexts in which security incidents occur provides valuable insights for risk assessment and mitigation strategies.
Code
# Analyze attack contextscontext_counts = df['attack_context'].value_counts().reset_index()context_counts.columns = ['Attack Context', 'Count']# Sort by count for better visualizationcontext_counts = context_counts.sort_values('Count', ascending=False)# Create a column chartfig_context = px.bar( context_counts, x='Attack Context', y='Count', title='Security Incidents by Attack Context', height=450)# Set primary color for all barsfig_context.update_traces(marker_color=palette["primary"])# Improve layoutfig_context.update_layout( xaxis_title=None, yaxis_title="Number of Incidents", xaxis={'categoryorder': 'total descending', 'tickangle': 45})fig_context.show()
6.1 Analysis of Attack Contexts
The distribution of security incidents across different attack contexts reveals important patterns:
6.1.1 Combat/Crossfire
Most common context for security incidents globally
Indicates that being caught between opposing forces represents the highest risk
Often leads to complex injury patterns and challenging evacuation scenarios
6.1.2 Individual Attacks
Targeted violence against specific individuals or small groups
May be politically, criminally, or personally motivated
Requires different security approaches than combat avoidance
6.1.3 Ambushes
Planned attacks often targeting mobile personnel
Particularly dangerous due to the element of surprise
Can be mitigated through route analysis and convoy procedures
6.1.4 Raids
Organized attacks against specific facilities or compounds
Often involve multiple attackers and coordinated tactics
Physical security measures and response protocols are critical countermeasures
6.1.5 Detention
Formal or informal holding of personnel by various actors
May involve legal or extra-legal processes
Can lead to prolonged security incidents with complex resolution requirements
This understanding of attack contexts helps security managers develop appropriate: - Training programs tailored to likely threat scenarios - Standard operating procedures for different contexts - Resource allocation based on context-specific risks - Coordination mechanisms with relevant security actors
7 Nationals vs. Internationals: Casualty Patterns
The impact of security incidents varies significantly between local nationals and international personnel. Understanding these differences is crucial for developing appropriate security protocols.
Code
# Create data for stacked bar chart comparing nationals vs internationalscasualties_data = {'Category': ['Nationals', 'Internationals'],'Killed': [ df['nationals_killed'].sum(), df['internationals_killed'].sum() ],'Wounded': [ df['nationals_wounded'].sum(), df['internationals_wounded'].sum() ],'Kidnapped': [ df['nationals_kidnapped'].sum(), df['internationals_kidnapped'].sum() ]}# Convert to DataFrame and reshape for stackingcasualties_df = pd.DataFrame(casualties_data)casualties_long = pd.melt( casualties_df, id_vars=['Category'], value_vars=['Killed', 'Wounded', 'Kidnapped'], var_name='Status', value_name='Count')# Create a stacked bar chartfig_casualties = px.bar( casualties_long, x='Category', y='Count', color='Status', title='Casualties by Nationality Category (Nationals vs. Internationals)', barmode='stack', height=500, color_discrete_map={'Killed': palette["danger"], 'Wounded': palette["primary"], 'Kidnapped': palette["secondary"] })# Improve layoutfig_casualties.update_layout( xaxis_title=None, yaxis_title="Number of People", legend_title_text=None, title={'y': 0.95,'x': 0.5,'xanchor': 'center','yanchor': 'top' })fig_casualties.show()
7.1 Analysis of Casualty Patterns
The comparison between nationals and internationals reveals several important patterns:
7.1.1 Disparity in Impact
Local nationals typically suffer significantly higher casualties than international personnel
This disparity applies across all types of harm (killed, wounded, kidnapped)
The ratio of nationals to internationals affected often exceeds 10:1 in many contexts
7.1.2 Factors Contributing to Disparity
Numbers and Exposure: Locals far outnumber internationals in most areas
Access to Protection: Internationals often have enhanced security measures and evacuation options
Targeting Patterns: Some actors specifically avoid targeting internationals due to potential international consequences
Risk Profiles: Internationals may have more restricted movement in high-threat areas
7.1.3 Kidnapping Trends
Kidnapping represents a relatively small proportion of overall casualties
However, internationals face a disproportionate kidnapping risk in many contexts due to:
Higher perceived ransom value
Political leverage potential
Media attention
7.1.4 Implications for Security Planning
Different security protocols may be needed for national and international staff
Equity considerations in providing security resources
Need for context-specific risk assessments
Importance of inclusive security measures that protect all personnel
8 Gender Distribution of Affected Individuals
Understanding how security incidents affect different gender groups provides important insights into vulnerability patterns and protection needs.
Code
# Check if we have gender datagender_cols = ['gender_male', 'gender_female']# Sum gender countsgender_totals = {'Gender': ['Male', 'Female'],'Count': [ df['gender_male'].sum(), df['gender_female'].sum() ]}# Convert to DataFramegender_df = pd.DataFrame(gender_totals)# Create simple bar chartfig_gender = px.bar( gender_df, x='Gender', y='Count', title='Gender Distribution of Affected Individuals', height=450)# Set primary color for barsfig_gender.update_traces(marker_color=palette["primary"])# Improve layoutfig_gender.update_layout( xaxis_title=None, yaxis_title="Number of Individuals")fig_gender.show()
8.1 Analysis of Gender Patterns
The gender distribution of individuals affected by security incidents reveals several significant patterns:
8.1.1 Male Predominance
Males constitute the majority of individuals affected by security incidents
This pattern is consistent across different regions and incident types
The disparity may reflect different exposure levels due to gender roles in some contexts
8.1.2 Contributing Factors
Occupational Exposure: Males may be overrepresented in certain high-risk professions
Mobility Patterns: Gender differences in freedom of movement may affect exposure to risks
Targeting Patterns: In some contexts, males may be specifically targeted in certain types of attacks
Reporting Biases: Incidents affecting females may be underreported in some settings
8.1.3 Implications for Protection
Gender-sensitive security protocols are essential
Protection strategies should account for different risk profiles by gender
Gender analysis should be incorporated into security assessments
Training should address the specific needs and vulnerabilities of different gender groups
Understanding these gender dimensions helps organizations develop more effective and inclusive security strategies that protect all personnel regardless of gender.
9 Organizations Affected by Security Incidents
Different types of organizations face varying levels of security risk based on their mandates, visibility, and operational contexts.
Code
# Create analysis of organizational impact if we have that dataorg_cols = ['un', 'ingo', 'icrc', 'nrcs_and_ifrc', 'nngo', 'other']ifall(col in df.columns for col in org_cols):# Create a summary of total incidents by organization type org_totals = df[org_cols].sum().reset_index() org_totals.columns = ['Organization Type', 'Total Incidents']# Better labels for organization types org_labels = {'un': 'United Nations','ingo': 'International NGO','icrc': 'Int. Committee of Red Cross','nrcs_and_ifrc': 'National Red Cross/Red Crescent','nngo': 'National NGO','other': 'Other Organizations' }# Map the short names to full names org_totals['Organization'] = org_totals['Organization Type'].map(org_labels)# Sort by total incidents org_totals = org_totals.sort_values('Total Incidents', ascending=False)# Create a horizontal bar chart fig_orgs = px.bar( org_totals, y='Organization', x='Total Incidents', title='Security Incidents by Organization Type', height=500, orientation='h' )# Set all bars to primary color fig_orgs.update_traces(marker_color=palette["primary"])# Improve layout fig_orgs.update_layout( yaxis_title=None, xaxis_title="Number of Incidents", yaxis={'categoryorder': 'total ascending'}, title={'y': 0.95,'x': 0.5,'xanchor': 'center','yanchor': 'top' } ) fig_orgs.show()
9.1 Organizational Risk Profiles
Different organizations face varying security risks based on numerous factors:
9.1.1 United Nations (UN)
Risk Profile: High visibility, operates in conflict areas, associated with political mandates
Common Threats: Direct targeting, collateral damage in conflict, complex attacks
Vulnerabilities: Large footprint, political associations, high visibility
Protective Factors: Robust security apparatus, international legal protections
9.1.2 International NGOs (INGOs)
Risk Profile: Variable based on mandate, widespread operations in insecure environments
Common Threats: Robbery, collateral damage, kidnapping, targeted attacks based on perceived values
Vulnerabilities: Limited security resources compared to UN, often work in remote locations
Protective Factors: Community acceptance, perceived neutrality (for some)
9.1.3 International Committee of the Red Cross (ICRC)
Risk Profile: Works directly in conflict zones, protected emblem status
Common Threats: Misidentification, deliberate targeting despite protected status
Vulnerabilities: Proximity to conflict, access to detained persons
Protective Factors: Protected status under IHL, recognized emblem, long history of neutrality
9.1.4 National Red Cross/Red Crescent Societies
Risk Profile: Local presence in high-risk areas, first responders
Common Threats: Similar to ICRC but with higher exposure due to continuous local presence
Vulnerabilities: Less international protection than ICRC in practice
Protective Factors: Community integration, protected emblem
9.1.5 National NGOs (NNGOs)
Risk Profile: Deep community ties but often limited security resources
Common Threats: Criminality, being perceived as aligned with certain factions
Protective Factors: Strong local knowledge, community acceptance
9.1.6 Security Implications by Organization Type
Organization-specific protocols: Security measures should be tailored to each organization’s unique risk profile
Resource allocation: Security resources should be distributed equitably based on risk
Coordination mechanisms: Inter-organizational security collaboration enhances protection for all
Training requirements: Staff need organization-specific security training
10 Comparison of Actor Types
Understanding which actors are responsible for security incidents helps identify patterns of responsibility and develop appropriate mitigation strategies.
Code
# Filter only for Host State and Foreign actorsrelevant_actors = ['Host state', 'Foreign or coalition forces']actor_data = df[df['actor_type'].isin(relevant_actors)]# Check if we have data for these specific actorsiflen(actor_data) >0: actor_counts = actor_data['actor_type'].value_counts().reset_index() actor_counts.columns = ['Actor Type', 'Count']# Create a column chart fig_actors = px.bar( actor_counts, x='Actor Type', y='Count', title='Host State vs Foreign Actors in Security Incidents', height=450 )# Set primary color for all bars fig_actors.update_traces(marker_color=palette["primary"])# Improve layout fig_actors.update_layout( xaxis_title=None, yaxis_title="Number of Incidents" ) fig_actors.show()
10.1 Analysis of Actor Responsibility
This analysis focuses specifically on two major actor types responsible for security incidents: Host State forces and Foreign/coalition forces. This comparison reveals important patterns:
10.1.1 Host State vs. Foreign Forces
The relative frequency of incidents attributed to each actor type varies significantly by context
Understanding these patterns helps identify which forces represent the primary security concern in different areas
10.1.2 Implications for Security Planning
Context-specific approaches: Security strategies should reflect the predominant actor types in each area
Engagement strategies: Different approaches may be needed when engaging with different security